Constrained classification: The use of a priori information in cluster analysis

In many classification problems, one often possesses external and/or internal information concerning the objects or units to be analyzed which makes it appropriate to impose constraints on the set of allowable classifications and their characteristics. CONCLUS, or CONstrained CLUStering, is a new methodology devised to perform constrained classification in either an overlapping or nonoverlapping (hierarchical or nonhierarchial) manner. This paper initially reviews the related classification literature. A discussion of the use of constraints in clustering problems is then presented. The CONCLUS model and algorithm are described in detail, as well as their flexibility for use in various applications. Monte Carlo results are presented for two synthetic data sets with appropriate discussion of the resulting implications. An illustration of CONCLUS is presented with respect to a sales territory design problem where the objects classified are various Forbes-500 companies. Finally, the discussion section highlights the main contribution of the paper and offers some areas for future research.We wish to thank C. Mallows and J. D. Carroll for some helpful technical discussion and L. Clark and D. Art for their valuable computer assistance. We also wish to thank H. Pollak, R. Gnanadesikan, and J. Kettenring for their thorough reviews of a previous draft of this paper. Finally, we acknowledge helpful comments of the editor and two anonymous reviewers.     

Masked emotional priming beyond global valence activations

An immense body of research demonstrates that emotional facial expressions can be processed unconsciously. However, it has been assumed that such processing takes place solely on a global valence-based level, allowing individuals to disentangle positive from negative emotions but not the specific emotion. In three studies, we investigated the specificity of emotion processing under conditions of limited awareness using a modified variant of an affective priming task. Faces with happy, angry, sad, fearful, and neutral expressions were presented as masked primes for 33 ms (Study 1) or 14 ms (Studies 2 and 3) followed by emotional target faces (Studies 1 and 2) or emotional adjectives (Study 3). Participants’ task was to categorise the target emotion. In all three studies, discrimination of targets was significantly affected by the emotional primes beyond a simple positive versus negative distinction. Results indicate that specific aspects of emotions might be automatically disentangled in addition to valence, even under conditions of subjective unawareness.     

Adaptations in driver deceleration behaviour with automatic incident detection: A naturalistic driving study

Traffic congestion and crash rates can be reduced by introducing variable speed limits (VSLs) and automatic incident detection (AID) systems. Previous findings based on loop detector measurements have revealed that drivers reduce their speeds while approaching traffic congestion when the AID system is active. Notwithstanding these behavioural effects, most microscopic traffic flow models assessing the impact of VSLs do not describe driver response accurately.This study analyses the main factors that influence driver deceleration behaviour while approaching traffic congestion with and without VSLs. The Dutch VSL database was linked to the driver behaviour data collected in the UDRIVE naturalistic driving study. Driver engagement in secondary tasks and glance behaviour were extracted from the video data. Linear mixed-effects models predicting the characteristics of deceleration events were estimated.The results show that the maximum deceleration is high when approaching a slower leader, when driving at high speeds and short distance headways, and close to the beginning of traffic congestion. The minimum time headway is short when driving at high speeds and changing lanes. Certain drivers showed higher decelerations and shorter time headways than others. Controlled for these main factors, smaller maximum decelerations were found when the VSLs were present and visible, and when the gantries were within close proximity. These factors could be incorporated into microscopic traffic simulations to evaluate the impact of AID systems on traffic congestion more realistically. Further research is needed to clarify the link between engagement in secondary tasks, glance behaviour and deceleration behaviour.     

Safety of micro-mobility: Riders’ psychological factors and risky behaviors of cargo TTWs in China

Cargo two- or three-wheeled vehicles (TTWs), as a new form of micro-mobility, have become a popular mode of urban cargo transportation in China. Cargo TTW riders’ psychological factors and risky behaviors lead to a number of accidents. A questionnaire is designed by comprehensively considering these factors and behaviors of cargo TTW riders that includes eleven risk factors to quantitatively analyze the risky behaviors based on structural equation modeling (SEM). One thousand three hundred nineteen participants reported using cargo TTWs on a questionnaire distributed across the country. The characteristics of riding behavior data are analyzed to verify the three-layer risk theoretical framework of “Psychological factors (Personality traits/specific factors) - Psychological acceptability of risks (confidence/perception/attitude) - Risky behaviors”. The results show that anger has a strong direct effect on riding violations, while normlessness and altruism have a direct effect on riding errors. Workload has a weak but direct effect on risky behaviors, and riding feedback has a weak and mixed effect. In addition, high-risk groups are identified by analysis of variance (ANOVA) with rider population attributes. These quantitative analyses can help guide safety countermeasures to mitigate accidents involving cargo TTWs.     

Does change in ethical education influence core moral values? Towards history- and culture-aware morality model with application in automatic moral reasoning

In this study, we focus on ethical education as a means to improve artificial companion’s conceptualization of moral decision-making process in human users. In particular, we focus on automatically determining whether changes in ethical education influenced core moral values in humans throughout the century. We analyze ethics as taught in Japan before WWII and today to verify how much the pre-WWII moral attitudes have in common with those of contemporary Japanese, to what degree what is taught as ethics in school overlaps with the general population’s understanding of ethics, as well as to verify whether a major reform of the guidelines for teaching the school subject of “ethics” at school after 1946 has changed the way common people approach core moral questions (such as those concerning the sacredness of human life). We selected textbooks used in teaching ethics at school from between 1935 and 1937, and those used in junior high schools today (2019) and analyzed what emotional and moral associations such contents generated. The analysis was performed with an automatic moral and emotional reasoning agent and based on the largest available text corpus in Japanese as well as on the resources of a Japanese digital library. As a result, we found out that, despite changes in stereotypical view on Japan’s moral sentiments, especially due to historical events, past and contemporary Japanese share a similar moral evaluation of certain basic moral concepts, although there is a large discrepancy between how they perceive some actions to be beneficial to the society as a whole while at the same time being inconclusive when it comes to assessing the same action’s outcome on the individual performing them and in terms of emotional consequences. Some ethical categories, assessed positively before the war, while being associated with a nationalistic trend in education have also disappeared from the scope of interest of post- war society. The findings of this study support suggestions proposed by others that the development of personal AI systems requires supplementation with moral reasoning. Moreover, the paper builds upon this idea and further suggests that AI systems need to be aware of ethics not as a constant, but as a function with a correction on historical and cultural changes in moral reasoning.     

Treating a Chronically Depressed Adolescent Female Using the Cognitive Behavioral Analysis System of Psychotherapy: A Case Study

A case is presented of an adolescent female with double depression who was treated using the Cognitive Behavioral Analysis System of Psychotherapy (CBASP). CBASP is designed to teach a social problem-solving procedure called Situational Analysis (SA). Generalized treatment effects were measured through monitoring of diagnostic status, two administrations of the Minnesota Multiphasic Personality Inventory-Adolescent, weekly administrations of the Child Depression Inventory, and acquisition performance ratings on the SA task. The results showed that CBASP appeared to be an effective treatment. The double depressive disorder remitted, and the patient learned to identify behavioral consequences as well as target and attain her interpersonal goals. CBASP can be effective for adolescents, although modifications of the adult form of the therapy may be necessary.     

How Should a Speech Recognizer Work?

Although researchers studying human speech recognition (HSR) and automatic speech recognition (ASR) share a common interest in how information processing systems (human or machine) recognize spoken language, there is little communication between the two disciplines. We suggest that this lack of communication follows largely from the fact that research in these related fields has focused on the mechanics of how speech can be recognized. In Marr's (1982) terms, emphasis has been on the algorithmic and implementational levels rather than on the computational level. In this article, we provide a computational-level analysis of the task of speech recognition, which reveals the close parallels between research concerned with HSR and ASR. We illustrate this relation by presenting a new computational model of human spoken-word recognition, built using techniques from the field of ASR that, in contrast to current existing models of HSR, recognizes words from real speech input.     

医学伪科学产生的宗教学析因

我国以气功、人体科学名义出现的医学伪科学是一种宗教现象或准宗教,其组织形式为膜拜教团,教主与信徒之间是欺骗与被欺骗的关系。医学伪科学的产生有一定的文化学背景,对文化的过高评价造成中国传统文化的超科学假象。虽非真正的宗教,医学伪科学却可以填补宗教的空缺发挥替代作用,这是医学伪科学得以出现和发展的重要原因。     

青少年攻击行为与友谊质量的交叉滞后回归分析

该研究采用问卷法,对湖北省一所初级中学二年级青少年进行了一个学年的纵向追踪研究,使用Bjorkquist等人编制的直接和间接攻击量表(DIA)以及Parker和Asher编制的友谊质量问卷(FQQ)测查青少年的身体攻击和友谊质量。然后采用二元交叉滞后回归分析法考察了初中二、三年级学生在一个学年中的攻击行为与友谊质量(总问卷及分问卷得分)的关系。结果表明,在控制了前测"肯定与关心"得分后,身体攻击显著地负向预测了"肯定与关心"得分。在控制了前测的"冲突解决策略"得分后,攻击行为边缘显著地预测了后测的"冲突解决策略"得分。同时发现,后测"身体攻击"和"冲突与背叛"不存在显著的相关。